Search: “evaluation”

arXiv:2609.16936v1 Announce Type: cross Abstract: Large language model (LLM)-powered coding agents have made rapid progress in automating software engineering tasks, yet repository-level issue resolut

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Model Releases

ResLRP: The Role of Residual Cancellation in Attribution Instability in Vision Transformers

DGX agent

arXiv:2609.17152v1 Announce Type: cross Abstract: Vision Transformers (ViTs) are central to most modern vision models, yet obtaining input attributions that are fine-grained, faithful, and stable rema

model-releasesarxiv-cs-ai

16 Sept 2026

RiskChainBench: A Benchmark for Obfuscated Platform Message Restoration and Evidence-Grounded Web Investigation

arXiv:2609.16900v1 Announce Type: new Abstract: Platform abuse campaigns conceal redirection instructions with emojis, homophones, character decomposition, and redundant symbols, then route users thro

Robust Recurrent Reinforcement Learning under Evolving Hidden Disturbances with Application to Rover Wheel Slip

arXiv:2307.15931v3 Announce Type: replace Abstract: Reinforcement learning (RL) performs well in continuous-control tasks, but evolving hidden disturbances create partial observability: the agent must

safetyarxiv-cs-lg

SafeFlow: Real-Time Text-Driven Humanoid Whole-Body Control via Physics-Guided Rectified Flow and Selective Safety Gating

arXiv:2603.23983v2 Announce Type: replace-cross Abstract: Recent advances in real-time interactive text-driven motion generation have enabled humanoids to perform diverse behaviors. However, kinematic

Same Answer, Different Representations: Hidden instability in VLMs

arXiv:2602.06652v2 Announce Type: replace Abstract: The robustness of Vision Language Models (VLMs) is commonly assessed through output-level invariance, implicitly assuming that stable predictions re

researcharxiv-cs-ai

Sample-Conditioned Representation Selection for Audio Few-Shot Learning

arXiv:2609.17076v1 Announce Type: new Abstract: Few-shot audio classifiers may rely on foreground-background co-occurrences and fail when those correlations shift. On SpurAudio, the resulting represen

researcharxiv-cs-ai

SAVLA: Symmetry-Aware Vision-Language-Action Models for Robotic Manipulation

arXiv:2609.16641v1 Announce Type: new Abstract: Vision-language-action (VLA) models have become the dominant paradigm for language-conditioned robot manipulation. However, although images and language

safetyarxiv-cs-ro

ScienceBuddy: Recursive-in-Recursive Self-Improvement for Interactive Scientific Agents

arXiv:2609.17523v1 Announce Type: new Abstract: We introduce and release ScienceBuddy, an interactive scientific research workspace that brings continually improving scientific agents into researchers

SciNLP: A Domain-Specific Benchmark for Full-Text Scientific Entity and Relation Extraction in NLP

arXiv:2509.07801v5 Announce Type: replace Abstract: Structured information extraction from scientific literature is crucial for capturing core concepts and emerging trends in specialized fields. While

Self-reported archetypes and behavioral failures in Large Language Models

arXiv:2609.15998v1 Announce Type: new Abstract: Every large language model (LLM) has behavioral traits and moral preferences that comprise its character. Whether by design or as an emergent property o

Semantic-Spatial Agreement Verification for Mitigating Object Hallucination in Multimodal Large Language Models

arXiv:2609.17269v1 Announce Type: new Abstract: Multimodal large language models generate natural-language responses from visual inputs, yet may mention objects absent from an image. In medication ass

safetyarxiv-cs-cv

SITA: Learning Speaker-Invariant and Tone-Aware Speech Representations for Low-Resource Tonal Languages

arXiv:2601.09050v2 Announce Type: replace Abstract: Tonal low-resource languages are widely spoken but remain underserved by modern speech technologies. A central challenge is learning speech represen

researcharxiv-cs-cl

Skeletal Prototypes on Iterative Nerve Expansions

model-releasesarxiv-cs-lg

arXiv:2609.16170v1 Announce Type: new Abstract: Prototype reduction replaces a training set with a smaller representation, and the established methods return a finite set of points. We propose Skeleta

Smarter by the Moment: Environment-Driven Dynamic Policies for Continual LLM Improvement

arXiv:2609.16800v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable progress across diverse domains, but continual adaptation to evolving tasks and environments remai

safetyarxiv-cs-cl

Sparse MLLM Anchors, Dense Adaptation: Breaking the Self-Referential Loop in Wild Test-Time Adaptation

arXiv:2609.17040v1 Announce Type: new Abstract: Wild test-time adaptation (WTTA) updates a source model online under small test batches, concurrent distribution shifts, and time-varying class imbalanc

SSC-Priors: Exploring Semantic and Visibility Priors to Boost Lidar Semantic Scene Completion

model-releasesarxiv-cs-cv

arXiv:2609.17413v1 Announce Type: new Abstract: This paper investigates easy strategies to boost the performance of existing networks for lidar semantic scene completion (SSC) without requiring comple

Local Ai

StackTok: Accelerating VLMs Inference with Budget-Adaptive Visual Token Selection

arXiv:2609.16841v1 Announce Type: cross Abstract: Increasing image resolution produces ever-longer visual-token sequences in vision-language models (VLMs), substantially raising their inference cost.

local-aiarxiv-cs-ai

StalePO: Anchored Token-Level Preference Optimization using Legacy Post-Edits in Machine Translation

arXiv:2609.16340v1 Announce Type: new Abstract: Machine translation systems are periodically upgraded to stronger models, but the available preference signal is human post-edits of an older system's o

Swim-and-Breach at Palm Scale: A Rudder-Steered Two-Propeller Underwater Robot Platform with Differential-Thrust Pitch Control

arXiv:2609.17240v1 Announce Type: new Abstract: We present a palm-scale (65 mm, 34 g) swim-and-breach robot platform. Two vertically stacked propellers provide both propulsion and differential-thrust

researcharxiv-cs-ro

SWIM: Vision-Language-Grounded Soft Whole-Body Interactive Manipulation

arXiv:2609.17035v1 Announce Type: new Abstract: Soft and continuum robots enable manipulation through distributed body deformation and contact, yet translating language and visual context into executa

safetyarxiv-cs-ro

Agents

Symbolic Separation: Grounding Deep Agents in Knowledge Graphs for Trustworthy Operational Data Analytics

arXiv:2609.17107v1 Announce Type: new Abstract: Generative AI promises natural language access to the massive numerical telemetry of data centers and Industry 4.0 installations, yet text-to-query and

agentsarxiv-cs-ai

Symmetry-Aware Likelihood-Orbit Aggregation for Selective Left-Right Claim Verification

arXiv:2609.17004v1 Announce Type: new Abstract: Frozen vision-language models (VLMs) remain unreliable on fine-grained left-right claims, and raw claim likelihoods need not reliably rank verification

researcharxiv-cs-cv

TARC: Time-Adaptive Robotic Control

arXiv:2510.23176v2 Announce Type: replace-cross Abstract: Most robotic systems rely on fixed-frequency discrete-time controllers, creating a trade-off between the efficiency of low-frequency control a

safetyarxiv-cs-lg

Target-Language Generation in Multilingual Models: Activation Steering and Optimal Control

arXiv:2609.16967v1 Announce Type: new Abstract: Ensuring that multilingual language models generate coherent text in a specific target language is a major issue in multilingual language modeling. We d

researcharxiv-cs-cl

TEMPO: Learning Temporal Context for Dynamic Robot Manipulation

model-releasesarxiv-cs-cv

arXiv:2609.16864v1 Announce Type: cross Abstract: Vision-language-action (VLA) models have achieved impressive performance in quasi-static manipulation, but struggle in dynamic manipulation tasks beca

Test-Time Unlearning via Sparse Autoencoder

arXiv:2609.16229v1 Announce Type: cross Abstract: Machine unlearning aims to remove specific knowledge from a trained large language model (LLM) without retraining from scratch. Existing methods modif

Tutorials

The Imitation Game: When LLMs Learn to Reason Like Programs via Code-Centric Reasoning Data Synthesis

arXiv:2609.16076v1 Announce Type: cross Abstract: Large Language Models (LLMs) excel at programming tasks but frequently fail at deterministic, fine-grained reasoning in natural language, relying heav

tutorialsarxiv-cs-ai

The Immutable Past: Formalizing State Mutability and Conflict Resolution in Mutable RAG

arXiv:2609.16073v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) serves as the primary memory architecture for long-horizon autonomous agents. However, treating shared memory as

Toward Governance-Aware Autonomous GIS: A Narrative Review of Ethical and Privacy Risks in LLM-Enabled GeoAI

arXiv:2609.16232v1 Announce Type: new Abstract: Geospatial artificial intelligence (GeoAI) powered by large language models (LLMs) is expanding the capacity to query, generate, and interpret spatial i

Towards Detecting AI-Assisted Responses in Online Surveys

arXiv:2609.17317v1 Announce Type: new Abstract: The use of LLMs to complete online surveys impacts the validity of survey-based research, but detecting such usage remains underexplored. We introduce a

Towards Scalable RLVR: Multimodal Instruction Following Data Synthesis and Distillation

arXiv:2609.16059v1 Announce Type: new Abstract: Multimodal instruction following (MMIF) is crucial for building generalist agents. However, current training paradigms rely heavily on Supervised Fine-T

safetyarxiv-cs-cl

TrafficGamer: Reliable and Flexible Traffic Simulation for Safety-Critical Scenarios with Game-Theoretic Oracles

arXiv:2408.15538v4 Announce Type: replace Abstract: While modern Autonomous Vehicle (AV) systems can develop reliable driving policies under regular traffic conditions, they frequently struggle with s

Unified Heterogeneous Graph Neural Network solver for Power Flow, Optimal Power Flow and State Estimation

arXiv:2609.16738v1 Announce Type: cross Abstract: Power Flow (PF), Optimal Power Flow (OPF), and State Estimation (SE) are fundamental problems in power system analysis, but solving them is computatio

researcharxiv-cs-lg

Unifying Semantic Priors and High-Frequency Traces: Enhancing V-JEPA with Mixture-of-Experts for Robust Synthetic Image Forensics

model-releasesarxiv-cs-cv

arXiv:2609.16778v1 Announce Type: new Abstract: The unchecked proliferation of manipulated images on social media platforms has increased the spread of misinformation, posing a severe threat to public

Unsafe by Reciprocity: How Generation-Understanding Coupling Undermines Safety in Unified Multimodal Models

arXiv:2603.27332v2 Announce Type: replace Abstract: Recent advances in Large Language Models (LLMs) and Text-to-Image (T2I) models have led to the emergence of Unified Multimodal Models (UMMs), where

safetyarxiv-cs-cv

ViCo: Visual-oriented Coding with Self-Reflection for Chart Replication

arXiv:2609.16014v1 Announce Type: new Abstract: This paper addresses the challenge of generating high-quality academic charts that match the visual standards of human-authored papers. While existing A

researcharxiv-cs-cl

Weave: Learning Whole-Body Dexterous Loco-Manipulation from Human-Object Interactions

arXiv:2609.16683v1 Announce Type: cross Abstract: Learning humanoid-object interaction requires coordinating whole-body balance, locomotion, and dexterous hand contact to control both robot and object

World-Action Models for Robot Learning and Control: A Survey

arXiv:2609.16074v1 Announce Type: cross Abstract: Robots operating in open environments act under partial observability, physical constraints, and dynamic task contexts. Beyond mapping observations an

safetyarxiv-cs-cv

You Don't Need To Train: Agentic Heuristic Learning Studio for Executable Human Activity Recognition

arXiv:2609.16065v1 Announce Type: cross Abstract: Human activity recognition (HAR) is usually framed as gradient-based training of neural networks. Agentic Heuristic Learning (AHL) Studio explores a c

Z-Loss Backward Geometry in Dense Output Heads and Sparse Routers

arXiv:2609.16179v1 Announce Type: cross Abstract: Z-loss has been widely applied to the logits of language-model output heads and sparse mixture-of-experts routers. Z-loss constrains the softmax log-n

Applications

A Conservative OCR-Enabled Workflow for R214 Sodium Screening of South African Packaged Foods

arXiv:2609.15427v1 Announce Type: cross Abstract: Using food package images to monitor sodium and salt content against South Africa's R214 sodium limits is challenging when screening decisions require

applicationsarxiv-cs-ai

A Corpus-Aligned Uthmani-to-Standard Quranic Word Mapping and a Deterministic Recitation Validator

arXiv:2609.14967v1 Announce Type: new Abstract: Quranic text is distributed in two orthographic forms that are byte-level distinct: the Uthmani script used in every printed mushaf, and the Standard (I

A family of spectral conjugate gradient algorithms derived by least-squares approximations based on a modified quasi--Newton update with application to a revised robust binary classification model

model-releasesarxiv-cs-lg

arXiv:2609.13526v1 Announce Type: cross Abstract: We develop a spectral three-term modification of the classic Hestenes--Stiefel conjugate gradient algorithm, preserving its anti-jamming characteristi

Agents

A Hybrid Agentic AI Framework for Intelligent Supply Chain Analytics

arXiv:2609.13561v1 Announce Type: new Abstract: Efficient utilization of supply chain analytics for decision making remains a significant challenge for planners, as critical tasks such as database que

agentsarxiv-cs-ai

Agents

A Hybrid Dependency-Aware Framework for Task Decomposition and Dynamic Agent Generation in Oracle-to-PostgreSQL Migration

arXiv:2609.14413v1 Announce Type: cross Abstract: Large language model (LLM)-based database migration is often treated as direct code transformation, although enterprise Oracle systems contain heterog

agentsarxiv-cs-ai

A Language-Guided Multimodal Foundation Model for Zero-Shot and Multi-Task Brain Signal Analysis

arXiv:2609.15740v1 Announce Type: cross Abstract: Brain signal analysis is essential for both neuroscience research and clinical diagnostics, yet current approaches face critical limitations. End-to-e

A Machine Learning API for Earth Observation Data Cubes Based on openEO

arXiv:2609.13453v1 Announce Type: new Abstract: Earth Observation (EO) data are increasingly organized as spatio-temporal data cubes, while machine learning (ML) methods operate on tabular feature mat

researcharxiv-cs-lg